






'''simple docstring'''
import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
a_											=			logging.getLogger(__name__)





def 			_a(	):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__  :					Optional[Any]						=argparse.ArgumentParser(
          description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'''				)
      parser.add_argument('''--file_path''',						type=A_,						default='''data/dump.txt''',						help='''The path to the data.'''				)
      parser.add_argument('''--tokenizer_type''',						type=A_,						default='''bert''',						choices=['''bert''', '''roberta''', '''gpt2''']				)
      parser.add_argument('''--tokenizer_name''',						type=A_,						default='''bert-base-uncased''',						help='''The tokenizer to use.'''				)
      parser.add_argument('''--dump_file''',						type=A_,						default='''data/dump''',						help='''The dump file prefix.'''				)
      SCREAMING_SNAKE_CASE__  :					Optional[Any]						=parser.parse_args()

      logger.info(f"Loading Tokenizer ({args.tokenizer_name})"				)
      if args.tokenizer_type == "bert":
            SCREAMING_SNAKE_CASE__  :					Optional[Any]						=BertTokenizer.from_pretrained(args.tokenizer_name				)
            SCREAMING_SNAKE_CASE__  :					Any						=tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
            SCREAMING_SNAKE_CASE__  :					Any						=tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            SCREAMING_SNAKE_CASE__  :					str						=RobertaTokenizer.from_pretrained(args.tokenizer_name				)
            SCREAMING_SNAKE_CASE__  :					List[str]						=tokenizer.special_tokens_map['''cls_token''']  # `<s>`
            SCREAMING_SNAKE_CASE__  :					int						=tokenizer.special_tokens_map['''sep_token''']  # `</s>`
      elif args.tokenizer_type == "gpt2":
            SCREAMING_SNAKE_CASE__  :					Optional[Any]						=GPTaTokenizer.from_pretrained(args.tokenizer_name				)
            SCREAMING_SNAKE_CASE__  :					Optional[Any]						=tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
            SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

      logger.info(f"Loading text from {args.file_path}"				)
      with open(args.file_path,						'''r''',						encoding='''utf8'''				) as fp:
            SCREAMING_SNAKE_CASE__  :					str						=fp.readlines()

      logger.info('''Start encoding'''				)
      logger.info(f"{len(A_				)} examples to process."				)

      SCREAMING_SNAKE_CASE__  :					List[str]						=[]
      SCREAMING_SNAKE_CASE__  :					Optional[int]						=0
      SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=1_0_0_0_0
      SCREAMING_SNAKE_CASE__  :					List[Any]						=time.time()
      for text in data:
            SCREAMING_SNAKE_CASE__  :					Optional[Any]						=f"{bos} {text.strip()} {sep}"
            SCREAMING_SNAKE_CASE__  :					Optional[int]						=tokenizer.encode(A_,						add_special_tokens=A_				)
            rslt.append(A_				)

            iter += 1
            if iter % interval == 0:
                  SCREAMING_SNAKE_CASE__  :					int						=time.time()
                  logger.info(f"{iter} examples processed. - {(end-start):.2f}s/{interval}expl"				)
                  SCREAMING_SNAKE_CASE__  :					int						=time.time()
      logger.info('''Finished binarization'''				)
      logger.info(f"{len(A_				)} examples processed."				)

      SCREAMING_SNAKE_CASE__  :					List[str]						=f"{args.dump_file}.{args.tokenizer_name}.pickle"
      SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=tokenizer.vocab_size
      if vocab_size < (1 << 1_6):
            SCREAMING_SNAKE_CASE__  :					Optional[Any]						=[np.uintaa(A_				) for d in rslt]
      else:
            SCREAMING_SNAKE_CASE__  :					Dict						=[np.intaa(A_				) for d in rslt]
      random.shuffle(rslt_				)
      logger.info(f"Dump to {dp_file}"				)
      with open(A_,						'''wb'''				) as handle:
            pickle.dump(rslt_,						A_,						protocol=pickle.HIGHEST_PROTOCOL				)


if __name__ == "__main__":
  main()

import string

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			""""""
  for i in sequence:
    __magic_name__								=			ord(A_   )
    if 65 <= extract <= 90:
      output += chr(155 - extract   )
    elif 97 <= extract <= 122:
      output += chr(219 - extract   )
    else:
      output += i
  return output

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			string.ascii_letters
  __magic_name__								=			string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
  return "".join(
      letters_reversed[letters.index(A_   )] if c in letters else c for c in sequence   )

def a__       (			):






  '''simple docstring'''





  from timeit import timeit

  print("""Running performance benchmarks..."""   )
  __magic_name__								=			"""from string import printable ; from __main__ import atbash, atbash_slow"""
  print(f'''> atbash_slow(): {timeit('atbash_slow(printable)',	setup=A_   )} seconds'''   )
  print(f'''>      atbash(): {timeit('atbash(printable)',	setup=A_   )} seconds'''   )


if __name__ == "__main__":
    for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
        print(F'''{example} encrypted in atbash: {atbash(example)}''')
    benchmark()

